import asyncio import time from functools import partial from typing import (Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, AsyncIterator) from vllm.lora.request import LoRARequest from vllm.config import ModelConfig from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.llm_engine import LLMEngine from vllm.engine.ray_utils import initialize_cluster, ray from vllm.logger import init_logger from vllm.outputs import RequestOutput from vllm.sampling_params import SamplingParams logger = init_logger(__name__) class AsyncEngineDeadError(RuntimeError): pass def _raise_exception_on_finish(task: asyncio.Task, request_tracker: "RequestTracker") -> None: msg = ("Task finished unexpectedly. This should never happen! " "Please open an issue on Github.") try: try: task.result() except asyncio.CancelledError: return except Exception as exc: raise AsyncEngineDeadError( msg + " See stack trace above for the actual cause.") from exc raise AsyncEngineDeadError(msg) except Exception as exc: request_tracker.propagate_exception(exc) raise exc class AsyncStream: """A stream of RequestOutputs for a request that can be iterated over asynchronously.""" def __init__(self, request_id: str) -> None: self.request_id = request_id self._queue = asyncio.Queue() self._finished = False def put(self, item: RequestOutput) -> None: if self._finished: return self._queue.put_nowait(item) def finish(self) -> None: self._queue.put_nowait(StopAsyncIteration()) self._finished = True @property def finished(self) -> bool: return self._finished def __aiter__(self): return self async def __anext__(self) -> RequestOutput: result = await self._queue.get() if isinstance(result, Exception): raise result return result class RequestTracker: """Synchronous abstraction for tracking requests.""" def __init__(self) -> None: self._request_streams: Dict[str, AsyncStream] = {} self._finished_requests: asyncio.Queue[str] = asyncio.Queue() self._new_requests: asyncio.Queue[Tuple[AsyncStream, dict]] = asyncio.Queue() self.new_requests_event = None def __contains__(self, item): return item in self._request_streams def init_event(self): self.new_requests_event = asyncio.Event() def propagate_exception(self, exc: Exception, request_id: Optional[str] = None) -> None: """Propagate an exception to request streams (all if request_id is None).""" if request_id is not None: self._request_streams[request_id].put(exc) else: for stream in self._request_streams.values(): stream.put(exc) def process_request_output(self, request_output: RequestOutput, *, verbose: bool = False) -> None: """Process a request output from the engine.""" request_id = request_output.request_id self._request_streams[request_id].put(request_output) if request_output.finished: if verbose: logger.info(f"Finished request {request_id}.") self.abort_request(request_id) def add_request(self, request_id: str, **engine_add_request_kwargs) -> AsyncStream: """Add a request to be sent to the engine on the next background loop iteration.""" if request_id in self._request_streams: raise KeyError(f"Request {request_id} already exists.") stream = AsyncStream(request_id) self._new_requests.put_nowait((stream, { "request_id": request_id, **engine_add_request_kwargs })) self.new_requests_event.set() return stream def abort_request(self, request_id: str, *, verbose: bool = False) -> None: """Abort a request during next background loop iteration.""" if verbose: logger.info(f"Aborted request {request_id}.") self._finished_requests.put_nowait(request_id) if request_id not in self._request_streams or self._request_streams[ request_id].finished: # The request has already finished or been aborted. return self._request_streams[request_id].finish() def get_new_and_finished_requests(self) -> Tuple[List[Dict], Set[str]]: """Get the new requests and finished requests to be sent to the engine.""" new_requests: List[Dict] = [] finished_requests: Set[str] = set() while not self._finished_requests.empty(): request_id = self._finished_requests.get_nowait() finished_requests.add(request_id) self._request_streams.pop(request_id, None) while not self._new_requests.empty(): stream, new_request = self._new_requests.get_nowait() if stream.request_id in finished_requests: # The request has already been aborted. stream.finish() continue self._request_streams[stream.request_id] = stream new_requests.append(new_request) self.new_requests_event.clear() return new_requests, finished_requests async def wait_for_new_requests(self): await self.new_requests_event.wait() class _AsyncLLMEngine(LLMEngine): """Extension of LLMEngine to add async methods.""" async def step_async(self) -> List[RequestOutput]: """Performs one decoding iteration and returns newly generated results. The workers are ran asynchronously if possible. This function performs one decoding iteration of the engine. It first schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy. Then, it executes the model and updates the scheduler with the model outputs. Finally, it decodes the sequences and returns the newly generated results. """ seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule() if not scheduler_outputs.is_empty(): # Execute the model. all_outputs = await self._run_workers_async( "execute_model", driver_kwargs={ "seq_group_metadata_list": seq_group_metadata_list, "blocks_to_swap_in": scheduler_outputs.blocks_to_swap_in, "blocks_to_swap_out": scheduler_outputs.blocks_to_swap_out, "blocks_to_copy": scheduler_outputs.blocks_to_copy, }) # Only the driver worker returns the sampling results. output = all_outputs[0] else: output = [] return self._process_model_outputs(output, scheduler_outputs) async def encode_request_async( self, request_id: str, # pylint: disable=unused-argument prompt: Optional[str], prompt_token_ids: Optional[List[int]] = None, lora_request: Optional[LoRARequest] = None, ): if prompt_token_ids is None: assert prompt is not None prompt_token_ids = await self.tokenizer.encode_async( request_id=request_id, prompt=prompt, lora_request=lora_request) return prompt_token_ids async def add_request_async( self, request_id: str, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]] = None, arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, prefix_pos: Optional[int] = None, ) -> None: if lora_request is not None and not self.lora_config: raise ValueError(f"Got lora_request {lora_request} but LoRA is " "not enabled!") if arrival_time is None: arrival_time = time.time() prompt_token_ids = await self.encode_request_async( request_id=request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, lora_request=lora_request) return self.add_request( request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, sampling_params=sampling_params, arrival_time=arrival_time, lora_request=lora_request, prefix_pos=prefix_pos, ) async def _run_workers_async( self, method: str, *args, driver_args: Optional[List[Any]] = None, driver_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> Any: """Runs the given method on all workers.""" coros = [] if driver_args is None: driver_args = args if driver_kwargs is None: driver_kwargs = kwargs # Run the driver worker asynchronously. driver_executor = getattr(self.driver_worker, method) coros.append(asyncio.get_event_loop().run_in_executor( None, partial(driver_executor, *driver_args, **driver_kwargs))) # Run the ray workers asynchronously. for worker in self.workers: coros.append(worker.execute_method.remote(method, *args, **kwargs)) all_outputs = await asyncio.gather(*coros) return all_outputs class AsyncLLMEngine: """An asynchronous wrapper for LLMEngine. This class is used to wrap the LLMEngine class to make it asynchronous. It uses asyncio to create a background loop that keeps processing incoming requests. The LLMEngine is kicked by the generate method when there are requests in the waiting queue. The generate method yields the outputs from the LLMEngine to the caller. NOTE: For the comprehensive list of arguments, see `LLMEngine`. Args: worker_use_ray: Whether to use Ray for model workers. Required for distributed execution. Should be the same as `parallel_config.worker_use_ray`. engine_use_ray: Whether to make LLMEngine a Ray actor. If so, the async frontend will be executed in a separate process as the model workers. log_requests: Whether to log the requests. start_engine_loop: If True, the background task to run the engine will be automatically started in the generate call. *args: Arguments for LLMEngine. *kwargs: Arguments for LLMEngine. """ _engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine def __init__(self, worker_use_ray: bool, engine_use_ray: bool, *args, log_requests: bool = True, max_log_len: Optional[int] = None, start_engine_loop: bool = True, **kwargs) -> None: self.worker_use_ray = worker_use_ray self.engine_use_ray = engine_use_ray self.log_requests = log_requests self.max_log_len = max_log_len self.engine = self._init_engine(*args, **kwargs) self.background_loop = None # We need to keep a reference to unshielded # task as well to prevent it from being garbage # collected self._background_loop_unshielded = None self.start_engine_loop = start_engine_loop self._request_tracker = RequestTracker() @property def is_running(self) -> bool: return (self.background_loop is not None and not self.background_loop.done()) def start_background_loop(self) -> None: """Start the background loop.""" if self.is_running: raise RuntimeError("Background loop is already running.") self._request_tracker.init_event() self._background_loop_unshielded = asyncio.get_event_loop( ).create_task(self.run_engine_loop()) self._background_loop_unshielded.add_done_callback( partial(_raise_exception_on_finish, request_tracker=self._request_tracker)) self.background_loop = asyncio.shield(self._background_loop_unshielded) def _init_engine(self, *args, **kwargs) -> Union[_AsyncLLMEngine, "ray.ObjectRef"]: if not self.engine_use_ray: engine_class = self._engine_class elif self.worker_use_ray: engine_class = ray.remote(num_cpus=0)(self._engine_class).remote else: # FIXME(woosuk): This is a bit hacky. Be careful when changing the # order of the arguments. cache_config = args[1] parallel_config = args[2] if parallel_config.tensor_parallel_size == 1: num_gpus = cache_config.gpu_memory_utilization else: num_gpus = 1 engine_class = ray.remote(num_gpus=num_gpus)( self._engine_class).remote return engine_class(*args, **kwargs) async def engine_step(self) -> bool: """Kick the engine to process the waiting requests. Returns True if there are in-progress requests.""" new_requests, finished_requests = ( self._request_tracker.get_new_and_finished_requests()) for new_request in new_requests: # Add the request into the vLLM engine's waiting queue. # TODO: Maybe add add_request_batch to reduce Ray overhead if self.engine_use_ray: await self.engine.add_request.remote(**new_request) else: await self.engine.add_request_async(**new_request) if finished_requests: await self._engine_abort(finished_requests) if self.engine_use_ray: request_outputs = await self.engine.step.remote() else: request_outputs = await self.engine.step_async() # Put the outputs into the corresponding streams. for request_output in request_outputs: self._request_tracker.process_request_output( request_output, verbose=self.log_requests) return len(request_outputs) > 0 async def _engine_abort(self, request_ids: Iterable[str]): if self.engine_use_ray: await self.engine.abort_request.remote(request_ids) else: self.engine.abort_request(request_ids) async def run_engine_loop(self): # Initialize the RequestTracker here so it uses the right event loop. has_requests_in_progress = False while True: if not has_requests_in_progress: await self._request_tracker.wait_for_new_requests() has_requests_in_progress = await self.engine_step() await asyncio.sleep(0) async def add_request( self, request_id: str, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]] = None, arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, prefix_pos: Optional[int] = None, ) -> AsyncStream: if self.log_requests: shortened_prompt = prompt shortened_token_ids = prompt_token_ids if self.max_log_len is not None: if shortened_prompt is not None: shortened_prompt = shortened_prompt[:self.max_log_len] if shortened_token_ids is not None: shortened_token_ids = shortened_token_ids[:self. max_log_len] logger.info(f"Received request {request_id}: " f"prompt: {shortened_prompt!r}, " f"prefix_pos: {prefix_pos}," f"sampling params: {sampling_params}, " f"prompt token ids: {shortened_token_ids}, " f"lora_request: {lora_request}.") if not self.is_running: if self.start_engine_loop: self.start_background_loop() else: raise AsyncEngineDeadError( "Background loop is not running. If it was running, " "inspect the output to find the stacktrace of the " "error that caused the background loop to stop " "(AsyncEngineDeadError).") if arrival_time is None: arrival_time = time.time() prompt_token_ids = await self.engine.encode_request_async( request_id=request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, lora_request=lora_request) stream = self._request_tracker.add_request( request_id, prompt=prompt, sampling_params=sampling_params, prompt_token_ids=prompt_token_ids, arrival_time=arrival_time, lora_request=lora_request, prefix_pos=prefix_pos) return stream async def generate( self, prompt: Optional[str], sampling_params: SamplingParams, request_id: str, prompt_token_ids: Optional[List[int]] = None, lora_request: Optional[LoRARequest] = None, prefix_pos: Optional[int] = None, ) -> AsyncIterator[RequestOutput]: """Generate outputs for a request. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the LLMEngine and streams the outputs from the LLMEngine to the caller. Args: prompt: The prompt string. Can be None if prompt_token_ids is provided. sampling_params: The sampling parameters of the request. request_id: The unique id of the request. prompt_token_ids: The token IDs of the prompt. If None, we use the tokenizer to convert the prompts to token IDs. lora_request: LoRA request to use for generation, if any. prefix_pos: If not None, we use the given position as the prefix position for each prompt. We will cache the prefix's KV cache and reuse it for the next request with the same prefix. This is an experimental feature, and may be replaced with automatic prefix caching in the future. Yields: The output `RequestOutput` objects from the LLMEngine for the request. Details: - If the engine is not running, start the background loop, which iteratively invokes :meth:`~vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step` to process the waiting requests. - Add the request to the engine's `RequestTracker`. On the next background loop, this request will be sent to the underlying engine. Also, a corresponding `AsyncStream` will be created. - Wait for the request outputs from `AsyncStream` and yield them. Example: >>> # Please refer to entrypoints/api_server.py for >>> # the complete example. >>> >>> # initialize the engine and the example input >>> engine = AsyncLLMEngine.from_engine_args(engine_args) >>> example_input = { >>> "prompt": "What is LLM?", >>> "stream": False, # assume the non-streaming case >>> "temperature": 0.0, >>> "request_id": 0, >>> } >>> >>> # start the generation >>> results_generator = engine.generate( >>> example_input["prompt"], >>> SamplingParams(temperature=example_input["temperature"]), >>> example_input["request_id"]) >>> >>> # get the results >>> final_output = None >>> async for request_output in results_generator: >>> if await request.is_disconnected(): >>> # Abort the request if the client disconnects. >>> await engine.abort(request_id) >>> # Return or raise an error >>> ... >>> final_output = request_output >>> >>> # Process and return the final output >>> ... """ # Preprocess the request. # This should not be used for logging, as it is monotonic time. arrival_time = time.monotonic() try: stream = await self.add_request( request_id, prompt, sampling_params, prompt_token_ids=prompt_token_ids, arrival_time=arrival_time, lora_request=lora_request, prefix_pos=prefix_pos, ) async for request_output in stream: yield request_output except (Exception, asyncio.CancelledError) as e: # If there is an exception or coroutine is cancelled, abort the # request. self._abort(request_id) raise e async def abort(self, request_id: str) -> None: """Abort a request. Abort a submitted request. If the request is finished or not found, this method will be a no-op. Args: request_id: The unique id of the request. """ if not self.is_running: raise AsyncEngineDeadError( "Background loop is not running. If it was running, " "inspect the output to find the stacktrace of the " "error that caused the background loop to stop " "(AsyncEngineDeadError).") return self._abort(request_id) def _abort(self, request_id: str) -> None: """Abort a request. Abort a submitted request. If the request is finished or not found, this method will be a no-op. Args: request_id: The unique id of the request. """ self._request_tracker.abort_request(request_id, verbose=self.log_requests) async def get_model_config(self) -> ModelConfig: """Get the model configuration of the vLLM engine.""" if self.engine_use_ray: return await self.engine.get_model_config.remote() else: return self.engine.get_model_config() @classmethod def from_engine_args(cls, engine_args: AsyncEngineArgs, start_engine_loop: bool = True) -> "AsyncLLMEngine": """Creates an async LLM engine from the engine arguments.""" # Create the engine configs. engine_configs = engine_args.create_engine_configs() parallel_config = engine_configs[2] # Initialize the cluster. placement_group = initialize_cluster(parallel_config, engine_args.engine_use_ray) # Create the async LLM engine. engine = cls(parallel_config.worker_use_ray, engine_args.engine_use_ray, *engine_configs, placement_group, log_requests=not engine_args.disable_log_requests, log_stats=not engine_args.disable_log_stats, max_log_len=engine_args.max_log_len, start_engine_loop=start_engine_loop) return engine async def do_log_stats(self) -> None: if self.engine_use_ray: await self.engine.do_log_stats.remote() else: self.engine.do_log_stats()